In related art, a technology called pose estimation, which estimates an own position and posture relative to a recognition target as an own pose is known. As such pose estimation, mainly, pose estimation using object recognition and pose estimation using environment recognition are widely known.
In the pose estimation using object recognition, an input image is photographed using a recognition target as a subject, and a pose is estimated through matching between feature points detected from the obtained input image and a dictionary obtained by learning the recognition target in advance.
Meanwhile, in the pose estimation using environment recognition, a three-dimensional map indicating positions of portions of a subject in three-dimensional space, which are characteristic in an ambient environment, is generated by learning an environment around the recognition target online, that is, in real time. That is, the three-dimensional map is sequentially updated. Then, a pose is estimated from the three-dimensional map obtained through learning and feature points detected from an input image obtained through photographing.
As such pose estimation using environment recognition, for example, simultaneously localization and mapping (SLAM), or the like, is known.
Further, as a technology relating to pose estimation, there is also a technology of recognizing an object on an input image by registering a feature amount of local feature points of a subject image which is made a recognition target in an image feature database and comparing the registered feature amount with a feature amount of local feature points detected from a photographed input image.
As such a technology of recognizing an object on an image, a technology of removing unnecessary local feature points on the basis of density of local feature points so that a feature amount of local feature points which are uniformly distributed on a subject image is registered in the image feature database, has been also proposed (see, for example, Patent Literature 1). According to this technology, it is possible to reduce a data amount of the image feature database.